torchquantum and tensorcircuit
Both are quantum software frameworks designed for simulating quantum circuits and machine learning, making them **competitors** as they offer similar functionalities for developing and deploying quantum algorithms, with `torchquantum` having broader support for real quantum computer deployment and `tensorcircuit` focusing on tensor network optimizations.
About torchquantum
mit-han-lab/torchquantum
A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers.
Supports statevector and pulse-level GPU simulation scaling to 30+ qubits, with dynamic computation graphs enabling interactive debugging. Integrates seamlessly with PyTorch's autograd for automatic gradient computation and batch tensorized processing, plus Qiskit for hardware deployment. Distinguishes itself through trainable parameterized gates, hybrid classical-quantum model construction, and measurement strategies supporting both analytical and stochastic sampling.
About tensorcircuit
tencent-quantum-lab/tensorcircuit
Tensor network based quantum software framework for the NISQ era
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